diff --git a/.gitattributes b/.gitattributes
index 1ef325f1b111266a6b26e0196871bd78baa8c2f3..28df5f900b358436f0267334b3e3e9af33f917ba 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -9,7 +9,6 @@
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
 *.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mds filter=lfs diff=lfs merge=lfs -text
 *.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
@@ -54,6 +53,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
-# Video files - compressed
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.webm filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..893372e2927511365c96ee2540612286d8b3e414
--- /dev/null
+++ b/README.md
@@ -0,0 +1,284 @@
+---
+pretty_name: English MLS
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- multilingual
+paperswithcode_id: multilingual-librispeech
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+- text-to-audio
+configs:
+- config_name: default
+  data_files:
+  - split: dev
+    path: data/dev-*
+  - split: test
+    path: data/test-*
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: book_id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 249688889.909
+    num_examples: 3807
+  - name: test
+    num_bytes: 245938961
+    num_examples: 3769
+  - name: train
+    num_bytes: 707578913096
+    num_examples: 10808037
+  download_size: 705179367357
+  dataset_size: 708074540946.909
+---
+
+
+
+# Dataset Card for English MLS
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+  - [How to use](#how-to-use)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
+- **Repository:** [Needs More Information]
+- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
+- **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
+
+### Dataset Summary
+
+This is a streamable version of the **English version of the Multilingual LibriSpeech (MLS) dataset**. 
+The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
+
+MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 
+8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages.
+
+This dataset card includes the 44.5K hours of English. Refers to this [dataset card](https://huggingface.co/datasets/facebook/multilingual_librispeech) for the other languages. 
+
+### Supported Tasks and Leaderboards
+
+- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
+- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
+
+### How to use
+
+The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. 
+
+For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
+```python
+from datasets import load_dataset
+
+mls = load_dataset("parler-tts/mls_eng", split="train")
+```
+
+Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
+```python
+from datasets import load_dataset
+
+mls = load_dataset("parler-tts/mls_eng", split="train", streaming=True)
+
+print(next(iter(mls)))
+```
+
+*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
+
+Local:
+
+```python
+from datasets import load_dataset
+from torch.utils.data.sampler import BatchSampler, RandomSampler
+
+mls = load_dataset("parler-tts/mls_eng", split="train")
+batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
+dataloader = DataLoader(mls, batch_sampler=batch_sampler)
+```
+
+Streaming:
+
+```python
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+mls = load_dataset("parler-tts/mls_eng", split="train", streaming=True)
+dataloader = DataLoader(mls, batch_size=32)
+```
+
+To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
+
+### Example scripts
+
+Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
+
+## Dataset Structure
+
+### Data Fields
+
+- file: A filename .flac format.
+
+- audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+
+- text: the transcription of the audio file.
+
+- id: unique id of the data sample.
+
+- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
+
+- chapter_id: id of the audiobook chapter which includes the transcription.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
+
+### Citation Information
+
+```
+@article{Pratap2020MLSAL,
+  title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
+  author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
+  journal={ArXiv},
+  year={2020},
+  volume={abs/2012.03411}
+}
+```
+
+
+### Data Statistics
+
+| Duration (h) | Train     | Dev   | Test  |
+|--------------|-----------|-------|-------|
+| English      | 44,659.74 | 15.75 | 15.55 |
+| German       | 1,966.51  | 14.28 | 14.29 |
+| Dutch        | 1,554.24  | 12.76 | 12.76 |
+| French       | 1,076.58  | 10.07 | 10.07 |
+| Spanish      | 917.68    | 9.99  | 10    |
+| Italian      | 247.38    | 5.18  | 5.27  |
+| Portuguese   | 160.96    | 3.64  | 3.74  |
+| Polish       | 103.65    | 2.08  | 2.14  |
+
+| # Speakers | Train |      | Dev |    | Test |    |
+|------------|-------|------|-----|----|------|----|
+|       Gender   | M     | F    | M   | F  | M    | F  |
+| English    | 2742  | 2748 | 21  | 21 | 21   | 21 |
+| German     | 81    | 95   | 15  | 15 | 15   | 15 |
+| Dutch      | 9     | 31   | 3   | 3  | 3    | 3  |
+| French     | 62    | 80   | 9   | 9  | 9    | 9  |
+| Spanish    | 36    | 50   | 10  | 10 | 10   | 10 |
+| Italian    | 22    | 43   | 5   | 5  | 5    | 5  |
+| Portuguese | 26    | 16   | 5   | 5  | 5    | 5  |
+| Polish     | 6     | 5    | 2   | 2  | 2    | 2  |
+
+| # Hours / Gender | Dev  |      | Test |      |
+|------------------|------|------|------|------|
+|       Gender   | M    | F    | M    | F    |
+| English          | 7.76 | 7.99 | 7.62 | 7.93 |
+| German           | 7.06 | 7.22 | 7    | 7.29 |
+| Dutch            | 6.44 | 6.32 | 6.72 | 6.04 |
+| French           | 5.13 | 4.94 | 5.04 | 5.02 |
+| Spanish          | 4.91 | 5.08 | 4.78 | 5.23 |
+| Italian          | 2.5  | 2.68 | 2.38 | 2.9  |
+| Portuguese       | 1.84 | 1.81 | 1.83 | 1.9  |
+| Polish           | 1.12 | 0.95 | 1.09 | 1.05 |
+
+
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